Map Prior

Map priors, representing pre-existing knowledge about an environment, are increasingly used to improve the accuracy and efficiency of various mapping and perception tasks, particularly in autonomous driving and robotics. Current research focuses on integrating diverse map types (e.g., OpenStreetMap, HD maps, historical sensor data) into online mapping systems, often employing neural network architectures like transformers and diffusion models to effectively encode and utilize this prior information. This enhances the robustness of map construction and localization, especially in challenging conditions like GPS-denied environments or when dealing with incomplete sensor data, leading to safer and more reliable autonomous systems.